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Small cosmetic changes to CamemBERT model card

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Louis MARTIN 2020-04-18 02:53:45 -07:00 committed by Julien Chaumond
parent 4a94c062a4
commit c73c83b0e6
1 changed files with 12 additions and 11 deletions
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model_cards/camembert-base-README.md
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@ -6,9 +6,9 @@ language: french
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa architecture.
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions varying the number of parameters, the amount of pretraining data and the pretraining data source domains.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
@ -17,11 +17,11 @@ For further information or requests, please go to [Camembert Website](https://ca
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert` / `camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert` / `camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert` / `camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert` / `camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert` / `camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
@ -29,6 +29,7 @@ For further information or requests, please go to [Camembert Website](https://ca
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
camembert = CamembertModel.from_pretrained("camembert-base")
@ -40,7 +41,7 @@ camembert.eval() # disable dropout (or leave in train mode to finetune)
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask",model="camembert-base",tokenizer="camembert-base")
camembert_fill_mask = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base")
results = camembert_fill_mask("Le camembert est <mask> :)")
# results
#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},
@ -61,9 +62,9 @@ tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]
# NB: can do in one step : tokenize.encode("J'aime le camembert !")
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed to Camembert as a torch tensor (batch dim 1)
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
@ -79,7 +80,7 @@ embeddings, _ = camembert(encoded_sentence)
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert-base",config=config)
camembert = CamembertModel.from_pretrained("camembert-base", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)